Remote sensing involves gathering data and information about a physical system by measuring radiation, particles, and fields associated with components of the system using sensors located some distance away. One common application of remote sensing is weather forecasting performed using data from weather satellites and Doppler radar. These and other remote sensing approaches can be used to address a variety of problems in which a measuring device cannot be located within the object being studied, including medical imaging, planetary and solar physics, nuclear fusion diagnostics, and microscopy.
With respect to atmospheric remote sensing, in addition to radar and weather imaging, a number of less-familiar instruments and methods are also employed, including limb scanning. Before discussing limb scanning, three terms will be defined: “field-of-view” refers to the angular area (usually height and width) that is instantaneously sampled by a remote sensing device; “field-of-regard” refers to the total angular area observable by a sensor, which can be larger than the field-of-view for those sensors with steerable pointing capability; and “line-of-sight” refers to an unobstructed view from the sensor to the target of interest, typically centered in the field-of-view and extending toward infinity, along which signal is gathered from the target during a sample period.
FIG. 1 is a schematic showing remote sensing lines-of-sight 40, 42 from a limb scanner or sensor on a satellite 30, 32 at different locations moving in low Earth orbit. Reference numerals 30, 32 refer to different locations in space of the same satellite. The “limb” refers to the outer edge of the apparent disk of a planetary, stellar, or celestial body 20; the limb is merely the horizon, albeit a curved horizon on account of the distance or altitude of the viewer. Reference numerals 10, 12 refer to two limbs corresponding to the different locations of the sensor. Limb scanning is one technique for remotely sensing an atmosphere by measuring the photon intensity near the horizon as an instrument's field-of-view is angularly scanned in the radial (or vertical to the surface of the planetary, stellar, or celestial body 20) direction. Typically, limb scanning is performed from low-Earth-orbit satellites, but limb scanning can be performed using any sensor mounted on either a moving (e.g., satellite, plane, or balloon) or fixed (e.g., mountaintop) platform.
Limb scanners may employ a moving objective mirror or may slew the entire sensor on a gimballed platform to scan the instrument field-of-view across the atmospheric limb. Optimal limb scanning requires balancing the number of vertical samples per scan, the horizontal distance between scans, the integration time per sample, the physical spatial scales of the system being measured, the motion of the sensor platform, and the size and sensitivity of the instrument. Moreover, for any given instrument and physical system being measured, trade-offs can be made between vertical sampling and horizontal sampling characteristics.
Almost all limb scan data analyses performed to date use a very simplistic approach to retrieve atmospheric characteristics from the data. This approach relies upon the assumption that the atmospheric is horizontally stratified or, equivalently, that the atmospheric is spherically symmetrical. With the simplifying assumption of spherical symmetry and a stratified atmosphere, each limb scan can be inverted to characterize the atmospheric layers near the geographical locus of geometrical tangent points for the lines-of-sight. The atmospheric composition, temperature, etc. are assumed to vary in the vertical dimension only; hence, the term “1-D” retrievals.
A whole series of limb scans may be acquired as a satellite orbits the globe. Since atmospheres are known to exhibit latitude, longitude, and time-of-day variations, the assumption of spherical symmetry is flawed. Consequently, atmospheric retrievals from 1-D algorithms include a variety of artifacts and errors. The severity of these errors depends upon how much the atmosphere departs from spherical symmetry about the tangent location, i.e., upon the magnitude of gradients along the lines-of-sight. For highly structured atmospheric features, such as the nightside ionosphere, 1-D retrieval can be highly inaccurate.
Since the whole purpose of satellite remote sensing is to characterize the global variations of the atmosphere, the primary assumption of spherical symmetry is violated at the outset. Researchers have developed more sophisticated approaches to remote sensing that relax this simplistic assumption. In these approaches, an entire sequence of limb scans along a portion of an orbit may be inverted simultaneously. The atmospheric structure is assumed to vary both in altitude and along the orbit path; so, these are referred to as 2-D retrievals. This 2-D approach is called tomography, from the Greek word for “cut”, since a slice of the atmosphere is analyzed. This approach is wholly analogous to X-ray computer-assisted tomography used in medical imaging.
Applicant recognized that while tomographic analyses have addressed the problem of reducing artifacts and errors remote in sensing data, they are still limited by the data collection techniques used in the sensors themselves. Basically, the current generation of space sensors was not designed for 2-D tomographic operations: they have sensitivity limitations and data sampling limitations that are not optimal for tomographic analysis. The biggest problem for standard limb-scan techniques is that the horizontal in-track resolution is limited by the cadence of limb scans. The cadence of limb scans, in turn, is determined by the number of vertical samples and the dwell-time at each sample bin, which are driven by vertical resolution and signal-to-noise requirements.
Conventional limb scanning entails sweeping the field-of-view of a sensor in a single direction (i.e., either low- to high-altitudes or high- to low-altitudes) within a larger field-of-regard, such as shown in FIG. 2. FIG. 2 shows conventional limb scanning from orbit, which sweeps the field-of-view consecutively through a range of angles, the field of regard, as the sensor moves. This is usually accomplished with a smoothly-moving scan mirror or by gimballing the entire instrument. For tomographic applications, the scanning should be performed roughly within the plane of the sensor's motion. Measurements are sampled while the field-of-view is moving, so the maximum sampling time is limited by the desired spatial resolution and maximum acceptable blurring. The minimum sampling time is determined by signal-to-noise limitations. The field-of-regard is determined by the physics of the problem being studied, as it corresponds to the total altitude range measured by the sensor.
Much of the information for each sample location originates near the geometrical tangent point of each line-of-sight, on account of the exponential altitude dependence of atmospheric density; the tangent point occurs at the lowest altitude with the greatest atmospheric density, such as shown in FIG. 3. FIG. 3 shows three consecutive limb scans plotted in cylindrical coordinates (i.e., ground distance and altitude). In these transformed coordinates the round earth is seen as a flat line, and the lines-of-sight are curved. During each limb scan, the tangent point locations stack-up into nearly vertical/radial columns that are spaced apart equally according to the limb scan cadence such as shown in FIG. 4. FIG. 4 shows the tangent locations for the three consecutive limb scans shown in FIG. 3. The sampled regions comprise nearly vertical columns that are spaced according to the limb scan cadence. The intrinsic horizontal/in-track spacing of these vertical columns limits the attainable horizontal resolution in degrees of a limb scan sequence toRHORIZONTAL=PSCAN/PORBIT*360where PORBIT and PSCAN are the orbital and limb scan periods.
Moreover, the vertical dimension is frequently somewhat oversampled, because most limb scanners sample data at a constant rate throughout the full altitude range, irrespective of the actual physical scale heights characteristic of various layers of the atmosphere. Vertical oversampling and in-track undersampling is poorly-suited for atmospheric tomography.